> ## Documentation Index
> Fetch the complete documentation index at: https://docs.atomscale.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> Get up and running with Atomscale in three steps

Welcome to Atomscale. This guide walks you through connecting your process and
characterization data, extracting insights from it, and using those insights to improve your
outcomes from passive monitoring to real-time intervention.

## Connect → Analyze → Act

Using Atomscale follows a cycle with three steps:

<Steps>
  <Step title="Connect" icon="link">
    Bring your data into Atomscale via file upload, screen capture, file watcher, or the Python SDK. Atomscale's adapter models automatically process raw instrument data into structured, analysis-ready representations.

    [Connect your data →](/platform/get-started/connect)
  </Step>

  <Step title="Analyze" icon="chart-mixed">
    Compare runs against your full process history using learned similarity embeddings. Assess within-run uniformity down to individual layers. Monitor active growths in real time.

    [Start analyzing →](/platform/get-started/analyze)
  </Step>

  <Step title="Act" icon="bolt">
    Set up anomaly detection and drift alerts that operate on Atomscale's derived metrics. Respond to issues during a run, make immediate go/no-go decisions, and progressively close the loop toward automated process control.

    [Take action →](/platform/get-started/act)
  </Step>
</Steps>

## Prerequisites

Before you begin, ensure you have:

<Check>An Atomscale account with appropriate permissions.</Check>

<Check>
  Access to the data sources you want to connect: characterization files, process logs, or a live
  instrument GUI for screen capture.
</Check>

<Check>
  Network connectivity between your data sources and Atomscale (or files to upload for offline
  evaluation).
</Check>

<Note>
  Don't have an account yet? Contact your organization's Atomscale administrator or [reach out to
  our team](mailto:support@atomscale.ai).
</Note>

## What You'll Achieve

By the end of this guide, you will:

1. **Have Atomscale processing your data**, with adapter models extracting derived metrics
   like lattice spacing or composition predictions
   from your raw instrument data.
2. **Be able to compare any run against your process history** using similarity embeddings that capture the full process signature.
3. **Have real-time monitoring configured**, tracking active growths and receiving alerts when the process diverges from expected behavior.
4. **Understand the path to closed-loop control**, from operator-assisted decision-making through agent-based process intervention.

## Choose Your Path

<CardGroup cols={2}>
  <Card title="Start from the Beginning" icon="seedling" href="/platform/get-started/connect">
    Connect your first data source and follow the full guide.
  </Card>

  <Card title="Data Already Connected" icon="database" href="/platform/get-started/analyze">
    Skip to analysis if your team has already set up data connections.
  </Card>

  <Card title="Ready for Monitoring" icon="bell" href="/platform/get-started/act">
    Jump to alerts and process control if you're already familiar with Atomscale's analysis tools.
  </Card>

  <Card title="Just Exploring" icon="compass" href="/platform/solutions">
    Learn about what Atomscale can do before committing to setup.
  </Card>
</CardGroup>

Ready to begin? Let's [connect your first data source](/platform/get-started/connect).